Directional Price Forecasting in the Continuous Intraday Market under Consideration of Neighboring Products and Limit Order Books

Paper: arXiv 2509.04452

Authors: Timothée Hornek, Sergio Potenciano Menci, Ivan Pavić

Abstract

The increasing penetration of variable renewable energy and flexible demand technologies, such as electric vehicles and heat pumps, introduces significant uncertainty in power systems, resulting in greater imbalance; defined as the deviation between scheduled and actual supply or demand. Short-term power markets, such as the European continuous intraday market, play a critical role in mitigating these imbalances by enabling traders to adjust forecasts close to real time. Due to the high volatility of the continuous intraday market, traders increasingly rely on electricity price forecasting to guide trading decisions and mitigate price risk. However most electricity price forecasting approaches in the literature simplify the forecasting task. They focus on single benchmark prices, neglecting intra-product price dynamics and price signals from the limit order book. They also underuse high-frequency and cross-product price data. In turn, we propose a novel directional electricity price forecasting method for hourly products in the European continuous intraday market. Our method incorporates short-term features from both hourly and quarter-hourly products and is evaluated using German European Power Exchange data from 2024-2025. The results indicate that features derived from the limit order book are the most influential exogenous variables. In addition, features from neighboring products; especially those with delivery start times that overlap with the trading period of the target product; improve forecast accuracy. Finally, our evaluation of the value captured by our electricity price forecasting suggests that the proposed electricity price forecasting method has the potential to generate profit when applied in trading strategies.

Complexity vs Empirical Score

• Math Complexity: 6.0/10
• Empirical Rigor: 7.0/10
• Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper employs classification models and feature engineering with high-frequency data, requiring advanced statistical and machine learning techniques, while being evaluated on real, high-quality intraday market data with clear trading strategy backtesting implications.

Research Flowchart

  flowchart TD
  A["Research Goal<br>Directional Intraday Price Forecasting"] --> B{"Key Methodology"}
  B --> C["Feature Engineering<br>Limit Order Book & Neighboring Products"]
  B --> D["Model Training<br>High-Frequency Time Series Analysis"]
  
  subgraph E["Data Inputs"]
      C
      F["German EPEX SPOT Data 2024-2025"]
      G["Quarter-hourly & Hourly Products"]
  end
  
  E --> C
  C --> D
  
  D --> H{"Key Outcomes"}
  H --> I["LOB Features: Highest Impact"]
  H --> J["Neighboring Products: Overlap Critical"]
  H --> K["Trading Strategy: Profit Potential"]
  
  style A fill:#e1f5fe
  style H fill:#e8f5e8

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1 of 213 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.